OpenRaiser/Intern-Atlas huggingface dataset:
Rendering Natural Camera Bokeh Effect with Deep Learning
Venue: arXiv cs.CV 2020
Year: 2020

Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas. While DSLR and system camera lenses can render this effect naturally, mobile cameras are unable to produce shallow depth-of-field photos due to a very small aperture diameter of their optics. Unlike the current solutions simulating bokeh by applying Gaussian blur to image background, in this paper we propose to learn a realistic shallow focus technique directly from the photos produced by DSLR cameras. For this, we present a large-scale bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR with 50mm f/1.8 lenses. We use these images to train a deep learning model to reproduce a natural bokeh effect based on a single narrow-aperture image. The experimental results show that the proposed approach is able to render a plausible non-uniform bokeh even in case of complex input data with multiple objects. The dataset, pre-trained models and codes used in this paper are available on the project website.
Real-time single image depth perception in the wild with handheld devices
Venue: arXiv cs.CV 2020
Year: 2020

Depth perception is paramount to tackle real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image represents the most versatile solution, since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit its practical deployment: i) the low reliability when deployed in-the-wild and ii) the demanding resource requirements to achieve real-time performance, often not compatible with such devices. Therefore, in this paper, we deeply investigate these issues showing how they are both addressable adopting appropriate network design and training strategies -- also outlining how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time depth-aware augmented reality and image blurring with smartphones in-the-wild.
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus
Venue: arXiv cs.CL 2020
Year: 2020

Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to the fact that the training data on which models are built typically reflect the asymmetries of natural languages, gender bias included. Exclusively fed with textual data, machine translation is intrinsically constrained by the fact that the input sentence does not always contain clues about the gender identity of the referred human entities. But what happens with speech translation, where the input is an audio signal? Can audio provide additional information to reduce gender bias? We present the first thorough investigation of gender bias in speech translation, contributing with: i) the release of a benchmark useful for future studies, and ii) the comparison of different technologies (cascade and end-to-end) on two language directions (English-Italian/French).
Adapting Workflow Management Systems to BFT Blockchains -- The YAWL Example
Venue: arXiv cs.DC 2020
Year: 2020

Blockchain technology provides an auditable and tamper-proof distributed storage infrastructure for information records. This can be leveraged to support distributed workflow management. Compared to proof-of-work consensus, popularized by Bitcoin and Ethereum, blockchains based on BFT (byzantine fault tolerance) ordering consensus trade scalability for immediacy and finality of consensus. This makes them easier to use as distribution infrastructure, as applications need not be adapted to deal with eventual consistency and delayed consensus of proof-of-work blockchains. Hence, applications such as workflow engines can be easily ported to such a blockchain infrastructure to take advantage of their decentralized integrity assurance and information distribution model. In this paper we describe how the YAWL workflow engine can be used on a BFT based blockchain infrastructure to enable collaborative workflows across different organizations.
COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers
Venue: arXiv cs.CY 2020
Year: 2020

A number of groups, from governments to non-profits, have quickly acted to innovate the contact-tracing process: they are designing, building, and launching contact-tracing apps in response to the COVID-19 crisis. A diverse range of approaches exist, creating challenging choices for officials looking to implement contact-tracing technology in their community and raising concerns about these choices among citizens asked to participate in contact tracing. We are frequently asked how to evaluate and differentiate between the options for contact-tracing applications. Here, we share the questions we ask about app features and plans when reviewing the many contact-tracing apps appearing on the global stage.
Weakly Secure Symmetric Multilevel Diversity Coding
Venue: arXiv cs.IT 2020
Year: 2020

Multilevel diversity coding is a classical coding model where multiple mutually independent information messages are encoded, such that different reliability requirements can be afforded to different messages. It is well known that {\em superposition coding}, namely separately encoding the independent messages, is optimal for symmetric multilevel diversity coding (SMDC) (Yeung-Zhang 1999). In the current paper, we consider weakly secure SMDC where security constraints are injected on each individual message, and provide a complete characterization of the conditions under which superposition coding is sum-rate optimal. Two joint coding strategies, which lead to rate savings compared to superposition coding, are proposed, where some coding components for one message can be used as the encryption key for another. By applying different variants of Han's inequality, we show that the lack of opportunity to apply these two coding strategies directly implies the optimality of superposition coding. It is further shown that under a set of particular security constraints, one of the proposed joint coding strategies can be used to construct a code that achieves the optimal rate region.
Toward a standardized methodology for constructing quantum computing use cases
Venue: arXiv cs.AI 2020
Year: 2020

We propose a standardized methodology for developing and evaluating use cases for quantum computers and quantum inspired methods. This methodology consists of a standardized set of questions which should be asked to determine how and indeed if, near term quantum computing can play a role in a given application. Developing such a set of questions is important because it allows different use cases to be evaluated in a fair and objective way, rather than considering each case on an ad hoc basis which could lead to an evaluation which focuses on positives of a use case, while ignoring weaknesses. To demonstrate our methodology we apply it to a concrete use case, ambulance dispatch, and find that there are some ways in which near term quantum computing could be deployed sensibly, but also demonstrate some cases ways in which its use would not be advised. The purpose of this paper is to initiate a dialogue within the community of quantum computing scientists and potential end users on what questions should be asked when developing real world use cases.
Tailoring r-index for metagenomics
Venue: arXiv cs.DS 2020
Year: 2020

A basic problem in metagenomics is to assign a sequenced read to the correct species in the reference collection. In typical applications in genomic epidemiology and viral metagenomics the reference collection consists of set of species with each species represented by its highly similar strains. It has been recently shown that accurate read assignment can be achieved with $k$-mer hashing-based pseudoalignment: A read is assigned to species A if each of its $k$-mer hits to reference collection is located only on strains of A. We study the underlying primitives required in pseudoalignment and related tasks. We propose three space-efficient solutions building upon the document listing with frequencies problem. All the solutions use an $r$-index (Gagie et al., SODA 2018) as an underlying index structure for the text obtained as concatenation of the set of species, as well as for each species. Given $t$ species whose concatenation length is $n$, and whose Burrows-Wheeler transform contains $r$ runs, our first solution, based on a grammar-compressed document array with precomputed queries at non terminal symbols, reports the frequencies for the ${\tt ndoc}$ distinct documents in which the pattern of length $m$ occurs in ${\cal O}(m + \log(n){\tt ndoc}) $ time. Our second solution is also based on a grammar-compressed document array, but enhanced with bitvectors and reports the frequencies in ${\cal O}(m + ((t/w)\log n + \log(n/r)){\tt ndoc})$ time, over a machine with wordsize $w$. Our third solution, based on the interleaved LCP array, answers the same query in ${\cal O}(m + \log(n/r){\tt ndoc})$. We implemented our solutions and tested them on real-world and synthetic datasets. The results show that all the solutions are fast on highly-repetitive data, and the size overhead introduced by the indexes are comparable with the size of the $r$-index.
DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors
Venue: arXiv cs.CV 2020
Year: 2020

Disentangling the underlying feature attributes within an image with no prior supervision is a challenging task. Models that can disentangle attributes well provide greater interpretability and control. In this paper, we propose a self-supervised framework DisCont to disentangle multiple attributes by exploiting the structural inductive biases within images. Motivated by the recent surge in contrastive learning paradigms, our model bridges the gap between self-supervised contrastive learning algorithms and unsupervised disentanglement. We evaluate the efficacy of our approach, both qualitatively and quantitatively, on four benchmark datasets.
Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, a driver's intention prediction near a road intersection is proposed. Our approach uses a deep bidirectional Long Short-Term Memory (LSTM) with an attention mechanism model based on a hybrid-state system (HSS) framework. As intersection is considered to be as one of the major source of road accidents, predicting a driver's intention at an intersection is very crucial. Our method uses a sequence to sequence modeling with an attention mechanism to effectively exploit temporal information out of the time-series vehicular data including velocity and yaw-rate. The model then predicts ahead of time whether the target vehicle/driver will go straight, stop, or take right or left turn. The performance of the proposed approach is evaluated on a naturalistic driving dataset and results show that our method achieves high accuracy as well as outperforms other methods. The proposed solution is promising to be applied in advanced driver assistance systems (ADAS) and as part of active safety system of autonomous vehicles.
A Novel Topology-Guided Attack and Its Countermeasure Towards Secure Logic Locking
Venue: arXiv cs.CR 2020
Year: 2020

The outsourcing of the design and manufacturing of integrated circuits (ICs) in the current horizontal semiconductor integration flow has posed various security threats due to the presence of untrusted entities, such as overproduction of ICs, sale of out-of-specification/rejected ICs, and piracy of Intellectual Properties (IPs). Consequently, logic locking emerged as one of the prominent design for trust techniques. Unfortunately, these locking techniques are now inclined to achieve complete Boolean satisfiability (SAT) resiliency after the seminal work published in [47]. In this paper, we propose a novel oracle-less attack that is based on the topological analysis of the locked netlist even though it is SAT-resilient. The attack relies on identifying and constructing unit functions with a hypothesis key to be searched in the entire netlist to find its replica. The proposed graph search algorithm efficiently finds the duplicate functions in the netlist, making it a self-referencing attack. This proposed attack is extremely efficient and can determine the secret key within a few minutes. We have also proposed a countermeasure to make the circuit resilient against this topology-guided attack to progress towards a secure logic locking technique.
Composite Logconcave Sampling with a Restricted Gaussian Oracle
Venue: arXiv cs.LG 2020
Year: 2020

We consider sampling from composite densities on $\mathbb{R}^d$ of the form $dπ(x) \propto \exp(-f(x) - g(x))dx$ for well-conditioned $f$ and convex (but possibly non-smooth) $g$, a family generalizing restrictions to a convex set, through the abstraction of a restricted Gaussian oracle. For $f$ with condition number $κ$, our algorithm runs in $O \left(κ^2 d \log^2\tfrac{κd}ε\right)$ iterations, each querying a gradient of $f$ and a restricted Gaussian oracle, to achieve total variation distance $ε$. The restricted Gaussian oracle, which draws samples from a distribution whose negative log-likelihood sums a quadratic and $g$, has been previously studied and is a natural extension of the proximal oracle used in composite optimization. Our algorithm is conceptually simple and obtains stronger provable guarantees and greater generality than existing methods for composite sampling. We conduct experiments showing our algorithm vastly improves upon the hit-and-run algorithm for sampling the restriction of a (non-diagonal) Gaussian to the positive orthant.
Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence
Venue: arXiv cs.CY 2020
Year: 2020

Modern cyber-physical systems (CPS), such as our energy infrastructure, are becoming increasingly complex: An ever-higher share of Artificial Intelligence (AI)-based technologies use the Information and Communication Technology (ICT) facet of energy systems for operation optimization, cost efficiency, and to reach CO2 goals worldwide. At the same time, markets with increased flexibility and ever shorter trade horizons enable the multi-stakeholder situation that is emerging in this setting. These systems still form critical infrastructures that need to perform with highest reliability. However, today's CPS are becoming too complex to be analyzed in the traditional monolithic approach, where each domain, e.g., power grid and ICT as well as the energy market, are considered as separate entities while ignoring dependencies and side-effects. To achieve an overall analysis, we introduce the concept for an application of distributed artificial intelligence as a self-adaptive analysis tool that is able to analyze the dependencies between domains in CPS by attacking them. It eschews pre-configured domain knowledge, instead exploring the CPS domains for emergent risk situations and exploitable loopholes in codices, with a focus on rational market actors that exploit the system while still following the market rules.
Montreal AI Ethics Institute's Response to Scotland's AI Strategy
Venue: arXiv cs.CY 2020
Year: 2020

In January and February 2020, the Scottish Government released two documents for review by the public regarding their artificial intelligence (AI) strategy. The Montreal AI Ethics Institute (MAIEI) reviewed these documents and published a response on 4 June 2020. MAIEI's response examines several questions that touch on the proposed definition of AI; the people-centered nature of the strategy; considerations to ensure that everyone benefits from AI; the strategy's overarching vision; Scotland's AI ecosystem; the proposed strategic themes; and how to grow public confidence in AI by building responsible and ethical systems. In addition to examining the points above, MAIEI suggests that the strategy be extended to include considerations on biometric data and how that will be processed and used in the context of AI. It also highlights the importance of tackling head-on the inherently stochastic nature of deep learning systems and developing concrete guidelines to ensure that these systems are built responsibly and ethically, particularly as machine learning becomes more accessible. Finally, it concludes that any national AI strategy must clearly address the measurements of success in regards to the strategy's stated goals and vision to ensure that they are interpreted and applied consistently. To do this, there must be inclusion and transparency between those building the systems and those using them in their work.
RTEX: A novel methodology for Ranking, Tagging, and Explanatory diagnostic captioning of radiography exams
Venue: arXiv cs.CV 2020
Year: 2020

This paper introduces RTEx, a novel methodology for a) ranking radiography exams based on their probability to contain an abnormality, b) generating abnormality tags for abnormal exams, and c) providing a diagnostic explanation in natural language for each abnormal exam. The task of ranking radiography exams is an important first step for practitioners who want to identify and prioritize those radiography exams that are more likely to contain abnormalities, for example, to avoid mistakes due to tiredness or to manage heavy workload (e.g., during a pandemic). We used two publicly available datasets to assess our methodology and demonstrate that for the task of ranking it outperforms its competitors in terms of NDCG@k. For each abnormal radiography exam RTEx generates a set of abnormality tags alongside an explanatory diagnostic text to explain the tags and guide the medical expert. Our tagging component outperforms two strong competitor methods in terms of F1. Moreover, the diagnostic captioning component of RTEx, which exploits the already extracted tags to constrain the captioning process, outperforms all competitors with respect to clinical precision and recall.
Mental Workload and Language Production in Non-Native Speaker IPA Interaction
Venue: arXiv cs.HC 2020
Year: 2020

Through proliferation on smartphones and smart speakers, intelligent personal assistants (IPAs) have made speech a common interaction modality. Yet, due to linguistic coverage and varying levels of functionality, many speakers engage with IPAs using a non-native language. This may impact the mental workload and pattern of language production displayed by non-native speakers. We present a mixed-design experiment, wherein native (L1) and non-native (L2) English speakers completed tasks with IPAs through smartphones and smart speakers. We found significantly higher mental workload for L2 speakers during IPA interactions. Contrary to our hypotheses, we found no significant differences between L1 and L2 speakers in terms of number of turns, lexical complexity, diversity, or lexical adaptation when encountering errors. These findings are discussed in relation to language production and processing load increases for L2 speakers in IPA interaction.
Provenance for Linguistic Corpora Through Nanopublications
Venue: arXiv cs.CL 2020
Year: 2020

Research in Computational Linguistics is dependent on text corpora for training and testing new tools and methodologies. While there exists a plethora of annotated linguistic information, these corpora are often not interoperable without significant manual work. Moreover, these annotations might have evolved into different versions, making it challenging for researchers to know the data's provenance. This paper addresses this issue with a case study on event annotated corpora and by creating a new, more interoperable representation of this data in the form of nanopublications. We demonstrate how linguistic annotations from separate corpora can be reliably linked from the start, and thereby be accessed and queried as if they were a single dataset. We describe how such nanopublications can be created and demonstrate how SPARQL queries can be performed to extract interesting content from the new representations. The queries show that information of multiple corpora can be retrieved more easily and effectively because the information of different corpora is represented in a uniform data format.
The high-order block RIP for non-convex block-sparse compressed sensing
Venue: arXiv cs.IT 2020
Year: 2020

This paper concentrates on the recovery of block-sparse signals, which is not only sparse but also nonzero elements are arrayed into some blocks (clusters) rather than being arbitrary distributed all over the vector, from linear measurements. We establish high-order sufficient conditions based on block RIP to ensure the exact recovery of every block $s$-sparse signal in the noiseless case via mixed $l_2/l_p$ minimization method, and the stable and robust recovery in the case that signals are not accurately block-sparse in the presence of noise. Additionally, a lower bound on necessary number of random Gaussian measurements is gained for the condition to be true with overwhelming probability. Furthermore, the numerical experiments conducted demonstrate the performance of the proposed algorithm.
Synergetic Learning Systems: Concept, Architecture, and Algorithms
Venue: arXiv cs.NE 2020
Year: 2020

Drawing on the idea that brain development is a Darwinian process of ``evolution + selection'' and the idea that the current state is a local equilibrium state of many bodies with self-organization and evolution processes driven by the temperature and gravity in our universe, in this work, we describe an artificial intelligence system called the ``Synergetic Learning Systems''. The system is composed of two or more subsystems (models, agents or virtual bodies), and it is an open complex giant system. Inspired by natural intelligence, the system achieves intelligent information processing and decision-making in a given environment through cooperative/competitive synergetic learning. The intelligence evolved by the natural law of ``it is not the strongest of the species that survives, but the one most responsive to change,'' while an artificial intelligence system should adopt the law of ``human selection'' in the evolution process. Therefore, we expect that the proposed system architecture can also be adapted in human-machine synergy or multi-agent synergetic systems. It is also expected that under our design criteria, the proposed system will eventually achieve artificial general intelligence through long term coevolution.